Robot Hand RH8 with 19DoF

Human-inspired, Adult-size Dexterous Robot Hand

We use a adult-sized robot hand for learning grasping and object manipulation skills. The hand is mounted on our FRANKA EMIKA Panda robot

The hand has 19 degrees-of-freedom and uses 8 smart actuators for precise control (actuators contained inside the unit).

Under actuated design aims to provide the right balance between fine control and conformance to the shape of the objects.

Tactile Sensing

All actuators provide real time control and feedback of position, speed and current measurement (with direction), enabling inference of applied force.

Additional data including actuator temperature, (over)load status and PWM, a Palm ToF Distance sensor and optional Capacitive pads at the back of the palm complete the sensor array.

We also have five 3-axis force-torque sensors (FTS) (shown in the image) attached to each finger tip. The FTS measure contact force and shear forces with a resolution of 1mN / 0.1g.

 
 

Videos

  • Research videos using the robot will be presented here. 
 

Publications

2020

Xue, H; Boettger, S; Rottmann, N; Pandya, H; Bruder, R; Neumann, G; Schweikard, A; Rueckert, E

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings

International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

GitHub FRANKA EMIKA Panda, ROS

We are developing a repository for real-time control of the FRANKA EMIKA Panda 7-dof robot arm.

Our project is based on ROS and allows to teleoperate the robot arm in real-time using motion tracking data provided by OptiTrack’s Motive software

 

GitHub Project and Links

Videos

  • Research videos using the robot will be presented here. 
 

Publications

2020

Xue, H; Boettger, S; Rottmann, N; Pandya, H; Bruder, R; Neumann, G; Schweikard, A; Rueckert, E

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings

International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

Robot FRANKA EMIKA Panda

FRANKA EMIKA’s Panda robot arm is a complient, light-weight robot arm with seven degrees-of-freedom. 

We use the C++ libfranka library in our own ROS project for learning complex manipulation skills. 

Links

Videos

  • Research videos using the robot will be presented here. 
 

Publications

2020

Xue, H; Boettger, S; Rottmann, N; Pandya, H; Bruder, R; Neumann, G; Schweikard, A; Rueckert, E

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings

International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

GitHub LEGO Robotic EV3 Python

Dieses open-source Projekt enthält Tools und Demos für die Python-Entwicklung mit den Lego Mindstorms EV3 und EV3Dev Bricks. 

Die Inhalte sind verständlich aufbereitet und wir haben zahlreiche Tutorials und Aufgaben für Schüler*innen erstellt. 

GitHub Code & Links

Details to the Software Development

Dieser Einführungsvortrag beschreibt die grundlegenden Schritte um einen LEGO Roboter zu bauen und mit Python zu programmieren. 

Weitere Links und Tutorials

GitHub High-Accuracy Sensor Glove, ROS, Gazebo

Sensor gloves are gaining importance in tracking hand and finger movements in virtual reality applications as well as in scientific research. In this project, we developed  a low-budget, yet accurate sensor glove system that uses flex sensors for fast and efficient motion tracking. 

The contributions are ROS Interfaces, simulation models as well as motion modeling approaches. 

GitHub Code & Links

Details to the Software Development

The figure shows a simplified schematic diagram of the system architecture for our sensor glove design:

(a) Glove layout with sensor placements, the orange fields denote the flex sensors, while the IMU is marked as a green rectangle,

(b) Circuit board which is wired with the sensor glove, has 10 voltage dividers for reading each flex sensor connected to ADC pins of the microcontoller ESP32-S2 and the IMU is connected to I2C pins,

(c) The ESP32-S2 sends the raw data via WiFi as ROS messages to the computer, which allows a real-time visualization in Unity or Gazebo,

(d) Post-processing of the recorded data, e.g. learning probabilistic movement models and searching for similarities.

Publications

A research publication by Robin Denz, Rabia Demirci, M. Ege Cansev, Adna Bliek, Philipp Beckerle, Elmar Rueckert and Nils Rottmann is currently under review. 

CPS Research Seminar

Univ.-Prof. Dr. Elmar Rueckert is organizing this research seminar. Topics include research in AI, machine and deep learning, robotics, cyber-physical-systems and process informatics. 

Language:
English only

Presenters are leading invited external speakers, doctoral students, senior researcher, graduates and undergraduates. 

Upcoming Talks

August 10, 2021
August 13, 2021

Location & Time

  • Location: To be decided
  • Dates: To be decided

Past Talks

Nothing from December 1, 2020 to December 31, 2020.

Datenstrukturen und Algorithmen (708.031)

Univ.-Prof. Dr. Elmar Rueckert was teaching this course at the Technical University Graz in the winter semester in 2012/13 and in 2013/14.

Language:
German only

Link to the university's course page

Link to the course in the TUG online system.

Course Details

  • Elementare Datenstrukturen (Felder, Stapel, Schlange).
  • Asymptotische Laufzeitanalyse von Programmen (O-Notation).
  • Sortierverfahren (Einfügen, Auswahl, Quicksort, Mergesort, Heapsort, Fachverteilung, i-größte Zahl, Randomisierung, untere Laufzeitschranken).
  • Gestreute Speicherung (Hashing; Überläuferlisten, offene Adressierung, Hashfunktionen).
  • Suchmethoden (sequentiell, binär, interpolativ, quadratische Binärsuche).
  • Baumstrukturen (Binärbäume, (a-b)-Bäume, amortisierte Umstrukturierungskosten, optimale Suchbäume).
  • Dynamische Datenverwaltung (Wörterbuchproblem, Warteschlangenproblem, Union-Find Problem).
  • Algorithmische Techniken (Inkrementelles Einfügen, Elimination, Divide & Conquer, dynamisches Programmieren, Randomisierung).

Mit bis zu 390 Teilnehmern*innen pro Vorlesung.

Literature

  • Cormen, Leiserson, Rivest: Introduction to Algorithms, MIT Press, London, 1990.

Probabilistic Learning for Robotics (RO5601)

Univ.-Prof. Dr. Elmar Rueckert was teaching this course at the University of Luebeck in the winter semester in 2018.

Language:
English only

Course Details

[WS2018/19] In the winter semester, I will teach a course on Probabilistic Learning for Robotics which covers advanced topics including graphical models, factor graphs, probabilistic inference for decision making and planning, and computational models for inference in neuroscience. The lecture will take place in the Seminarraum Informatik 5 (Von Neumann) 2.132 from 12.00 – 14.00 on selected Thursdays.

In accompanying exercises and hands on tutorials the students will experiment with state of the art machine learning methods and robotic simulation tools. In particular, Mathworks’ MATLAB,  the robot middleware ROS and the simulation tool V-Rep will be used. The exercises and tutorials will also take place in the seminar room  2.132 on selected Fridays (see the course materials and dates below).

Prerequisites (recommended) 

  • Humanoid Robotics (RO5300)
  • Robotics (CS2500)

Follow this link to register for the course: https://moodle.uni-luebeck.de/course/view.php?id=3793.

Location & Time: Room: Seminarraum Informatik 5 (Von Neumann) 2.132 12.15 – 14.00

Course materials and dates

  1. Probabilistic Learning for Robotics Intro (L1: October, 18th)
  2. Introductions to Topics I-III: Bayesian Inference, Gaussian Processes & Kalman/P. Filters (L2: October, 25th)
  3. Introductions to Topics IV-VI: Bayesian Optimization, Spiking Networks for Planning, Probabilistic Movement Primitives (L3: November, 1st)